SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning (2026.findings-acl)
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| Challenge: | Existing training pipelines sample training examples uniformly across steps or epochs, ignoring differences in difficulty, redundancy, and learning value, which slows learning and wastes computation. |
| Approach: | They propose a self-paced learning framework that enables efficient learning based on the capability of the model being trained through optimizing which data to use and when. |
| Outcome: | The proposed framework achieves comparable or better accuracy than state-of-the-art baselines while using up to (100 times) fewer samples. |
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